Biology subjects

Schachtman, D. P.

Publications and source records attributed to Schachtman, D. P..

5 recordsLinked to original sources

bioRxiv2025

Strigolactone effects on Sorghum bicolor ecophysiology and symbioses

Strigolactones are ecologically, developmentally, and physiologically important hormones, but much remains unknown about their evolution and role in non-model species. Sorghum is a globally important C4 cereal and exhibits natural variation in root-exuded strigolactones. Differences in sorghum strigolactone stereochemistry are associated with resistance to parasitic plants, but with evidence for potential trade-offs. We studied sorghum mutants of loci in the strigolactone biosynthetic pathway, CAROTENOID CLEAVAGE DIOXYGENASE 8 (SbCCD8b) and LOW GERMINATION STIMULANT 1 (LGS1), previously shown to be Striga resistant by stimulating little germination of the parasite. SbCCD8b CRISPR-Cas9 deletions changed the accumulation of low abundance metabolites, reduced net carbon assimilation rate, altered root architecture and anatomy, and diminished the establishment and benefit of mycorrhizal symbionts. For Striga-resistant LGS1 CRISPR-Cas9 deletions, differentially expressed genes were enriched with promoter motifs for stress response and growth pathways, net carbon assimilation rate was reduced, and the colonization of mycorrhizal symbionts was delayed. We additionally restored functional LGS1 into the RTx430 genetic background, which normally has the lgs1-2 natural deletion allele. While root exudates from LGS1 insertion mutants rescued Striga susceptibility, we did not see consistent rescue of other traits impacted in LGS1 loss-of-function mutants. We hypothesize that epistasis with a neighboring strigolactone synthesis gene, which is rarely lost without concomitant loss of LGS1, may alter the phenotypic effects of LGS1 variation. Our study gives context to potential trade-offs associated with host resistance to parasitic plants and, more broadly, builds on the contribution of strigolactones in shaping sorghum physiological processes, growth, and development.

plant biology↗

bioRxiv2023

Root associated bacterial communities and root metabolite composition are linked to nitrogen use efficiency in sorghum

Development of cereal crops with high nitrogen-use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improve crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse sorghum lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography-mass spectrometry (GC-MS) were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly influenced the root-associated bacterial communities and root metabolite composition of sorghum. Sorghum NUE was positively correlated with the bacterial richness and diversity in the rhizosphere. The greater alpha diversity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxa, Pseudomonas. Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to N stress and indicate that the shift in the sorghum microbiome due to low-N is associated with the root metabolites of the host plant. Taken together, our study provides new insight into the links between host genetic regulation of root metabolites and root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low-N stress.

microbiology↗

bioRxiv2022

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression. While genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7,614 genes. Rare allele burden within genomic intervals with trans-eQTLs correlated with extremes of expression in target genes as previously reported for cis-eQTLs. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in phytohormone pathways, cell wall biosynthesis, lipid metabolism and stress response. Several genes involved in cell proliferation, flower development, DNA replication and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

bioinformatics↗

bioRxiv2020

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

BackgroundMicrobiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. ResultsIn this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. ConclusionsStandardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.

microbiology↗

bioRxiv2017

High-Throughput Profiling Identifies Resource Use Efficient And Abiotic Stress Tolerant Sorghum Varieties

Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G x E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G x E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.

plant biology↗